Updated: 2020-09-01 07:39:49 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
South Dakota 1.51 13291 362
Iowa 1.29 65698 1262
North Dakota 1.24 11876 295
Vermont 1.24 1608 9
West Virginia 1.22 10198 143
Delaware 1.21 17122 76
Montana 1.18 7420 142
Alabama 1.17 126344 1455
Minnesota 1.16 75667 868
Nebraska 1.15 34096 304
Missouri 1.14 76299 1249
Kansas 1.13 43146 667
South Carolina 1.12 118939 961
Maine 1.11 4533 28
Connecticut 1.10 52692 147
Ohio 1.10 123274 1114
Oklahoma 1.10 58643 775
Arkansas 1.08 60498 632
New Hampshire 1.08 7275 21
Kentucky 1.07 51020 713
Indiana 1.06 96478 1047
North Carolina 1.06 168300 1696
Virginia 1.06 95090 742
New York 1.05 439431 653
Tennessee 1.05 151744 1488
Wisconsin 1.05 76681 775
Pennsylvania 1.04 138785 672
Utah 1.04 52200 390
Michigan 1.03 113127 818
Illinois 1.02 236235 2004
Louisiana 1.02 148090 700
Maryland 1.01 108790 540
New Jersey 1.01 193336 300
Oregon 1.00 26754 234
Washington 1.00 77772 514
New Mexico 0.99 25402 134
Idaho 0.97 32368 283
Massachusetts 0.96 127039 344
Colorado 0.95 57866 303
California 0.94 713050 5119
Georgia 0.94 252848 2186
Nevada 0.94 69422 487
Texas 0.93 642330 4702
Mississippi 0.92 83327 671
Wyoming 0.92 3847 34
Florida 0.91 623480 2966
Arizona 0.87 202125 474
Rhode Island 0.82 19929 71

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Whitman WA 20 1 1.9 525 1080 60
King WA 1 2 1.0 19717 910 126
Spokane WA 5 3 1.2 5345 1070 44
Benton WA 6 4 1.1 4339 2230 24
Clark WA 8 5 1.1 2589 560 23
Whatcom WA 12 6 1.3 1105 510 6
Snohomish WA 4 7 0.9 7077 900 34
Yakima WA 2 9 0.9 11680 4680 26
Grant WA 9 10 0.9 2368 2500 29
Pierce WA 3 11 0.8 7514 870 40
Franklin WA 7 12 1.0 4079 4500 14
OR
county ST case rank severity R_e cases cases/100k daily cases
Lane OR 8 1 1.4 709 190 9
Malheur OR 6 2 1.1 1183 3890 21
Marion OR 2 3 1.0 3833 1140 42
Hood River OR 18 4 1.6 232 1000 2
Multnomah OR 1 5 0.9 6024 750 44
Umatilla OR 4 6 1.1 2684 3490 18
Washington OR 3 7 1.0 3788 650 31
Clackamas OR 5 8 1.0 1957 480 18
Jackson OR 7 10 0.9 822 380 16
Deschutes OR 9 15 0.9 694 380 3
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 1.0 242093 2400 1294
San Joaquin CA 10 2 1.1 17608 2400 195
Stanislaus CA 12 3 1.1 14724 2730 171
San Diego CA 5 4 1.0 38652 1170 272
Sacramento CA 9 5 1.0 17938 1190 271
Orange CA 3 6 1.0 48724 1540 326
Butte CA 30 7 1.3 2004 880 54
San Bernardino CA 4 9 0.8 47822 2240 334
Riverside CA 2 12 0.8 52675 2210 272
Alameda CA 8 15 0.9 18279 1110 171
Fresno CA 7 16 0.8 25095 2570 230
Kern CA 6 18 0.8 29405 3330 148

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.9 133824 3150 254
Navajo AZ 5 2 1.2 5604 5160 14
Pinal AZ 4 3 0.9 9584 2280 49
Pima AZ 2 4 0.7 21297 2090 79
Yuma AZ 3 5 1.0 12242 5890 24
Coconino AZ 8 6 1.1 3324 2370 11
Mohave AZ 6 7 0.9 3624 1760 15
Apache AZ 7 9 0.8 3344 4680 5
Santa Cruz AZ 9 12 0.7 2757 5920 2
CO
county ST case rank severity R_e cases cases/100k daily cases
Denver CO 1 1 1.0 11278 1630 47
Delta CO 24 2 1.5 164 540 3
El Paso CO 4 3 1.0 6084 880 36
Adams CO 3 4 0.9 7600 1530 47
Larimer CO 9 5 1.1 1959 580 20
Jefferson CO 5 6 1.0 4804 840 27
Arapahoe CO 2 7 0.8 8261 1300 39
Weld CO 6 9 1.0 4067 1380 16
Boulder CO 7 11 1.0 2313 720 11
Douglas CO 8 12 0.8 2117 640 16
UT
county ST case rank severity R_e cases cases/100k daily cases
Utah UT 2 1 1.1 11013 1870 119
Salt Lake UT 1 2 1.0 24053 2150 154
Davis UT 3 3 1.1 3902 1150 39
Millard UT 14 4 1.7 147 1150 1
Weber UT 4 5 1.0 3289 1330 24
Tooele UT 8 6 1.2 692 1060 6
Sevier UT 18 7 1.6 94 450 1
Washington UT 5 9 1.0 2807 1750 12
Cache UT 6 10 1.0 2117 1730 10
Summit UT 7 14 0.6 850 2100 4
San Juan UT 9 16 0.9 666 4360 1
NM
county ST case rank severity R_e cases cases/100k daily cases
Chaves NM 10 1 1.2 728 1110 16
Luna NM 15 2 1.4 322 1330 7
Doña Ana NM 4 3 1.1 2843 1320 15
Bernalillo NM 1 4 0.9 5827 860 30
Eddy NM 13 5 1.1 498 870 9
McKinley NM 2 6 1.1 4201 5770 6
Lea NM 6 7 0.9 1140 1630 13
Sandoval NM 5 8 1.0 1253 890 7
San Juan NM 3 9 1.0 3195 2510 6
Santa Fe NM 8 12 0.8 828 560 6
Otero NM 7 19 0.7 1120 1700 1
Cibola NM 9 21 0.5 728 2700 1

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Bergen NJ 1 1 1.2 21824 2350 31
Somerset NJ 13 2 1.3 5483 1660 9
Ocean NJ 7 3 1.0 11246 1900 31
Monmouth NJ 8 4 1.1 10820 1740 19
Camden NJ 9 5 1.1 9212 1820 25
Essex NJ 2 6 1.0 20556 2590 26
Middlesex NJ 4 7 1.0 18657 2260 23
Union NJ 6 9 1.0 17264 3120 15
Passaic NJ 5 10 0.9 18493 3670 23
Hudson NJ 3 17 0.8 20304 3040 13
PA
county ST case rank severity R_e cases cases/100k daily cases
Columbia PA 27 1 1.7 652 980 22
Adams PA 28 2 1.6 639 630 11
Lackawanna PA 14 3 1.5 2079 980 14
Centre PA 32 4 1.5 476 290 10
Philadelphia PA 1 5 1.0 33762 2140 99
Allegheny PA 4 6 1.0 10342 840 63
Bucks PA 5 7 1.1 7798 1240 30
Lancaster PA 6 10 1.0 6730 1250 36
Montgomery PA 2 11 1.0 11036 1340 42
Chester PA 8 14 1.1 5657 1090 24
Lehigh PA 9 15 1.2 5201 1430 11
Delaware PA 3 17 0.9 10384 1840 39
Berks PA 7 18 1.0 6070 1460 31
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 1.0 15501 1870 107
Prince George’s MD 1 2 1.0 26613 2940 98
Anne Arundel MD 5 3 1.1 8359 1470 51
Montgomery MD 2 4 1.0 20046 1930 69
Caroline MD 18 5 1.5 506 1540 5
Baltimore city MD 4 6 0.9 14519 2360 63
Wicomico MD 11 7 1.2 1562 1530 14
Harford MD 8 8 1.0 2481 990 22
Charles MD 9 10 1.0 2398 1520 16
Frederick MD 7 11 1.0 3500 1410 19
Howard MD 6 12 1.0 4394 1390 21
VA
county ST case rank severity R_e cases cases/100k daily cases
Montgomery VA 33 1 1.5 485 490 18
Essex VA 75 2 1.8 124 1120 2
Fairfax VA 1 3 1.1 18400 1610 104
Prince George VA 30 4 1.5 538 1420 9
Pittsylvania VA 20 5 1.3 770 1250 19
Newport News city VA 9 6 1.2 2314 1280 28
Prince William VA 2 7 1.0 10841 2370 65
Loudoun VA 4 8 1.1 5979 1550 36
Henrico VA 6 10 1.1 4614 1420 37
Virginia Beach city VA 3 14 1.0 6030 1340 41
Arlington VA 8 15 1.0 3558 1530 23
Norfolk city VA 7 18 1.0 4396 1790 27
Chesterfield VA 5 24 0.9 5055 1490 26
WV
county ST case rank severity R_e cases cases/100k daily cases
Fayette WV 12 1 2.0 278 630 17
Kanawha WV 1 2 1.2 1415 760 28
Monroe WV 24 3 1.3 135 1000 12
Cabell WV 4 4 1.3 535 560 7
Mingo WV 15 5 1.4 240 970 4
Raleigh WV 7 6 1.3 358 470 5
Monongalia WV 2 7 1.1 1120 1060 10
Jefferson WV 6 9 1.2 358 640 5
Berkeley WV 3 14 1.1 802 710 5
Mercer WV 9 16 1.0 304 500 4
Wood WV 8 17 1.1 308 360 2
Logan WV 5 21 0.7 486 1440 6
DE
county ST case rank severity R_e cases cases/100k daily cases
Sussex DE 2 1 1.5 6330 2880 16
New Castle DE 1 2 1.2 8174 1470 50
Kent DE 3 3 1.1 2618 1500 10

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Lee AL 6 1 1.6 4621 2900 196
Shelby AL 7 2 1.5 4548 2150 91
Jefferson AL 1 3 1.3 16421 2490 190
Tuscaloosa AL 5 4 1.2 5942 2880 118
Baldwin AL 8 5 1.3 4537 2180 57
Madison AL 4 6 1.3 6462 1810 56
Chilton AL 30 7 1.4 1188 2700 28
Montgomery AL 3 8 1.2 7993 3520 55
Mobile AL 2 9 1.0 12271 2960 77
Marshall AL 9 52 0.7 3632 3820 13
MS
county ST case rank severity R_e cases cases/100k daily cases
Lafayette MS 15 1 1.4 1440 2690 40
Copiah MS 24 2 1.3 1089 3790 10
Oktibbeha MS 14 3 1.1 1442 2910 21
Madison MS 5 4 1.0 2978 2880 29
DeSoto MS 2 5 0.9 4637 2630 45
Harrison MS 3 6 1.0 3317 1640 35
Grenada MS 32 7 1.3 945 4440 7
Hinds MS 1 10 0.9 6548 2710 37
Jackson MS 4 11 0.9 3022 2130 32
Jones MS 8 15 1.0 2164 3160 12
Rankin MS 6 16 0.8 2872 1900 25
Lee MS 7 19 0.8 2189 2580 24
Forrest MS 9 23 0.9 2141 2840 14
LA
county ST case rank severity R_e cases cases/100k daily cases
Plaquemines LA 51 1 1.9 515 2200 6
Evangeline LA 31 2 1.7 1098 3260 16
Orleans LA 3 3 1.3 11478 2950 45
East Feliciana LA 30 4 1.2 1143 5860 48
Jackson LA 52 5 1.5 472 2960 8
Caddo LA 5 6 1.1 7418 2990 38
Grant LA 54 7 1.4 427 1910 8
Calcasieu LA 6 8 1.2 7412 3700 26
Jefferson LA 1 9 1.0 16454 3780 46
East Baton Rouge LA 2 10 1.0 13756 3100 58
St. Tammany LA 7 13 1.0 6044 2400 31
Lafayette LA 4 19 0.9 8268 3440 17
Ouachita LA 8 23 0.8 5485 3510 17
Tangipahoa LA 9 25 0.8 4037 3090 15

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Martin FL 26 1 1.4 4493 2850 60
Clay FL 29 2 1.4 4031 1940 55
Miami-Dade FL 1 3 0.8 157232 5790 601
Hillsborough FL 4 4 1.0 37157 2690 192
Palm Beach FL 3 5 1.0 42032 2910 186
Orange FL 5 6 1.0 35918 2720 160
Broward FL 2 7 0.8 71232 3730 251
Duval FL 6 8 1.0 26499 2870 119
Lee FL 8 9 1.0 18709 2600 90
Polk FL 9 10 1.0 17001 2540 100
Pinellas FL 7 19 0.9 19951 2080 66
GA
county ST case rank severity R_e cases cases/100k daily cases
Bulloch GA 29 1 1.8 1949 2610 81
Taylor GA 148 2 1.7 157 1920 7
Hall GA 5 3 1.2 7717 3940 93
Clarke GA 20 4 1.2 2900 2330 57
Pulaski GA 135 5 1.5 220 1950 9
Cobb GA 3 6 1.0 17039 2290 135
Cherokee GA 11 7 1.0 4888 2020 65
Gwinnett GA 2 11 0.8 24372 2700 145
Fulton GA 1 12 0.8 24995 2450 149
Chatham GA 6 18 1.0 7074 2460 50
DeKalb GA 4 27 0.8 16595 2230 77
Clayton GA 7 31 0.8 6527 2340 55
Richmond GA 8 33 0.8 6046 3000 48
Bibb GA 9 51 0.6 5542 3610 62

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Coryell TX 66 1 2.0 1002 1330 45
Brazos TX 22 2 1.7 5109 2330 124
Harris TX 1 3 1.1 106539 2310 1102
Walker TX 29 4 1.4 3657 5110 51
Bee TX 48 5 1.5 1598 4890 20
Callahan TX 189 6 1.8 91 660 6
Lubbock TX 18 7 1.2 7470 2480 80
Hidalgo TX 5 11 0.8 27936 3290 342
Bexar TX 3 12 1.0 46550 2420 169
Tarrant TX 4 20 0.9 41696 2060 219
El Paso TX 8 21 1.0 20466 2440 130
Cameron TX 7 22 0.9 21238 5040 188
Dallas TX 2 24 0.6 74395 2880 341
Nueces TX 9 27 0.8 18924 5250 98
Travis TX 6 45 0.6 26566 2210 85
OK
county ST case rank severity R_e cases cases/100k daily cases
Muskogee OK 10 1 2.3 1118 1620 100
Payne OK 7 2 1.5 1166 1430 40
Love OK 53 3 1.8 104 1050 3
Cleveland OK 3 4 1.1 3918 1420 55
Garfield OK 12 5 1.2 988 1590 34
Tulsa OK 2 6 0.9 13297 2070 119
Oklahoma OK 1 7 0.9 13471 1720 115
Rogers OK 5 10 1.1 1319 1450 15
Canadian OK 4 11 1.1 1541 1130 16
Texas OK 9 17 1.2 1147 5430 6
Wagoner OK 8 20 1.0 1150 1480 12
Comanche OK 6 36 0.7 1274 1040 19

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Luce MI 43 1 1.6 158 2480 16
Ottawa MI 9 2 1.4 2154 760 23
Isabella MI 26 3 1.3 505 710 29
Wayne MI 1 4 1.0 31351 1780 152
Branch MI 16 5 1.4 1297 2980 11
Calhoun MI 18 6 1.4 996 740 16
Oakland MI 2 7 1.0 18104 1450 106
Kent MI 4 8 1.1 8506 1320 56
Macomb MI 3 9 1.0 13090 1510 99
Jackson MI 7 16 1.2 2556 1610 8
Saginaw MI 8 19 0.9 2541 1320 22
Washtenaw MI 6 20 1.0 3420 930 16
Genesee MI 5 27 0.9 3977 970 14
WI
county ST case rank severity R_e cases cases/100k daily cases
Racine WI 5 1 1.4 4512 2310 89
Outagamie WI 7 2 1.3 1871 1010 45
Winnebago WI 11 3 1.3 1527 900 24
Forest WI 60 4 1.7 78 860 2
La Crosse WI 12 5 1.3 1187 1010 21
Portage WI 24 6 1.3 605 860 14
Milwaukee WI 1 7 0.9 24147 2530 114
Dane WI 3 10 1.1 5459 1030 47
Brown WI 4 12 0.9 5427 2090 60
Rock WI 8 13 1.2 1844 1140 19
Waukesha WI 2 18 0.9 5600 1400 45
Kenosha WI 6 33 0.9 2967 1760 11
Walworth WI 9 34 0.9 1711 1660 13

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Winona MN 24 1 1.8 432 850 23
Lyon MN 19 2 1.8 516 2000 13
Douglas MN 43 3 1.9 165 440 3
Hennepin MN 1 4 1.1 23129 1870 202
Blue Earth MN 11 5 1.5 1238 1870 28
Dakota MN 3 6 1.2 5937 1420 94
Pennington MN 59 7 1.9 87 610 2
Washington MN 6 8 1.2 3007 1190 54
Ramsey MN 2 9 1.1 9209 1700 83
Anoka MN 4 10 1.1 4733 1360 55
Olmsted MN 7 16 1.2 2031 1330 17
Scott MN 8 17 1.1 2009 1400 23
Stearns MN 5 22 1.1 3273 2090 24
Nobles MN 9 35 1.1 1866 8540 6
SD
county ST case rank severity R_e cases cases/100k daily cases
Clay SD 6 1 2.2 350 2510 38
Brookings SD 9 2 1.9 311 910 24
Pennington SD 2 3 1.6 1433 1310 62
Lawrence SD 11 4 1.7 237 940 22
Meade SD 7 5 1.6 316 1150 26
Codington SD 8 6 1.6 315 1130 18
Minnehaha SD 1 7 1.2 5260 2820 54
Brown SD 4 8 1.4 674 1740 20
Lincoln SD 3 11 1.3 900 1640 17
Beadle SD 5 19 1.2 639 3480 4
ND
county ST case rank severity R_e cases cases/100k daily cases
Stutsman ND 9 1 2.1 206 980 13
Barnes ND 18 2 1.9 112 1030 10
Grand Forks ND 3 3 1.3 1528 2170 75
Williams ND 7 4 1.6 433 1270 16
Cass ND 1 5 1.3 3529 2030 36
Burleigh ND 2 6 1.0 2033 2170 45
Stark ND 4 7 1.1 704 2280 25
Morton ND 5 8 1.2 646 2110 16
Ward ND 6 12 1.0 516 750 16
Benson ND 8 15 1.0 230 3340 4

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Hartford CT 3 1 1.1 13490 1510 50
New London CT 5 2 1.4 1568 580 8
Litchfield CT 4 3 1.4 1702 930 7
Tolland CT 7 4 1.4 1139 750 8
Fairfield CT 1 5 1.0 18876 2000 44
New Haven CT 2 6 1.0 13669 1590 23
Middlesex CT 6 7 1.2 1452 890 3
Windham CT 8 8 1.1 797 680 3
MA
county ST case rank severity R_e cases cases/100k daily cases
Middlesex MA 1 1 1.0 27676 1730 73
Suffolk MA 2 2 0.9 23506 2970 88
Barnstable MA 9 3 1.4 1854 870 5
Bristol MA 6 4 1.1 9786 1750 28
Essex MA 3 5 0.9 18894 2420 52
Plymouth MA 7 6 1.0 9631 1880 22
Norfolk MA 5 7 1.0 11034 1580 22
Hampden MA 8 8 1.0 7933 1690 17
Worcester MA 4 9 0.9 14187 1730 27
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 0.8 16761 2640 55
Kent RI 2 2 0.9 1677 1020 8
Newport RI 4 3 1.0 438 530 3
Bristol RI 5 4 1.0 352 720 2
Washington RI 3 5 0.8 701 560 4

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Otsego NY 47 1 2.0 140 230 3
Chautauqua NY 23 2 1.6 394 300 16
New York City NY 1 3 1.0 238926 2830 271
Sullivan NY 15 4 1.7 1525 2030 4
Nassau NY 3 5 1.1 44665 3290 54
Erie NY 7 6 1.1 9864 1070 54
Tompkins NY 36 7 1.6 264 260 3
Westchester NY 4 8 1.1 36976 3820 38
Suffolk NY 2 10 1.0 44808 3010 41
Rockland NY 5 14 1.1 14250 4400 16
Dutchess NY 9 16 1.0 4866 1660 13
Monroe NY 8 17 1.0 5457 730 18
Orange NY 6 19 1.0 11448 3030 12

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Rutland VT 4 1 1.7 117 200 2
Windsor VT 7 2 1.8 79 140 1
Chittenden VT 1 3 1.2 793 490 3
Addison VT 6 4 1.4 81 220 1
Bennington VT 5 5 1.2 102 280 1
Windham VT 2 6 0.8 124 290 1
Franklin VT 3 7 1.1 123 250 0
ME
county ST case rank severity R_e cases cases/100k daily cases
York ME 2 1 1.3 836 410 14
Androscoggin ME 3 2 1.2 613 570 3
Cumberland ME 1 3 1.0 2190 750 4
Kennebec ME 5 4 1.0 190 160 1
Penobscot ME 4 5 0.5 231 150 2
NH
county ST case rank severity R_e cases cases/100k daily cases
Hillsborough NH 1 1 1.2 4035 980 8
Rockingham NH 2 2 1.0 1811 590 6
Belknap NH 6 3 1.5 125 210 0
Cheshire NH 5 4 1.0 131 170 2
Grafton NH 7 5 1.2 114 130 1
Strafford NH 4 6 1.0 384 300 1
Carroll NH 8 7 1.0 105 220 1
Merrimack NH 3 8 0.8 503 340 2

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.5 11031 2700 176
Charleston SC 1 2 1.3 14032 3560 105
Greenville SC 2 3 1.1 12155 2440 70
Lexington SC 5 4 1.2 5753 2010 42
Berkeley SC 7 5 1.2 4822 2310 31
Dorchester SC 11 6 1.2 3627 2330 29
Horry SC 4 7 1.1 9383 2920 37
Spartanburg SC 6 8 1.0 5027 1660 43
Beaufort SC 8 13 1.0 4768 2610 29
Florence SC 9 17 0.9 4262 3080 31
NC
county ST case rank severity R_e cases cases/100k daily cases
Pamlico NC 86 1 2.1 141 1110 11
Pitt NC 10 2 1.3 3578 2020 131
Wake NC 2 3 1.0 15213 1450 180
Mecklenburg NC 1 4 1.0 25561 2420 160
Robeson NC 11 5 1.2 3543 2660 46
Guilford NC 4 6 1.1 6867 1310 68
Alamance NC 13 7 1.1 3169 1970 43
Durham NC 3 11 1.1 6956 2270 41
Forsyth NC 5 15 1.0 6175 1660 45
Gaston NC 6 18 1.0 4100 1890 37
Cumberland NC 7 23 1.0 4067 1220 42
Johnston NC 9 33 1.0 3875 2030 29
Union NC 8 37 0.9 3991 1760 35

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Rosebud MT 7 1 1.7 272 2940 24
Yellowstone MT 1 2 1.1 2088 1320 44
Flathead MT 4 3 1.1 608 620 17
Cascade MT 6 4 1.2 294 360 10
Big Horn MT 3 5 1.1 664 4960 10
Gallatin MT 2 6 1.1 1088 1040 7
Hill MT 17 7 1.2 86 520 4
Lake MT 8 8 1.3 203 680 1
Missoula MT 5 10 1.0 437 380 4
Lewis and Clark MT 9 14 0.6 197 290 1
WY
county ST case rank severity R_e cases cases/100k daily cases
Campbell WY 7 1 1.3 194 410 4
Uinta WY 5 2 1.4 297 1440 2
Sheridan WY 10 3 1.0 153 510 5
Natrona WY 6 4 1.0 293 360 3
Teton WY 3 5 1.0 432 1870 3
Laramie WY 2 6 0.9 572 590 3
Lincoln WY 12 7 1.2 112 590 1
Fremont WY 1 8 0.8 610 1520 4
Sweetwater WY 4 9 0.9 301 680 1
Park WY 9 10 0.8 162 560 1
Carbon WY 8 12 0.2 191 1230 0
ID
county ST case rank severity R_e cases cases/100k daily cases
Canyon ID 2 1 1.0 7236 3410 57
Bonneville ID 4 2 1.1 1827 1630 33
Ada ID 1 3 0.9 11208 2510 68
Fremont ID 26 4 1.4 117 900 2
Twin Falls ID 5 5 1.1 1685 2010 11
Payette ID 6 6 1.0 682 2960 16
Kootenai ID 3 7 1.0 2192 1430 13
Bannock ID 7 10 0.9 679 800 9
Blaine ID 8 18 1.1 606 2760 2
Jerome ID 9 21 0.9 604 2580 4

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Ashland OH 73 1 2.7 165 310 1
Montgomery OH 5 2 1.4 5769 1080 110
Butler OH 7 3 1.4 4024 1060 82
Franklin OH 1 4 1.1 21781 1710 161
Hamilton OH 3 5 1.2 11167 1380 84
Cuyahoga OH 2 6 1.1 15720 1250 100
Meigs OH 77 7 1.5 132 570 7
Summit OH 6 12 1.0 4469 820 44
Lucas OH 4 13 1.0 6432 1490 45
Mahoning OH 9 40 0.9 2862 1240 10
Marion OH 8 61 0.9 3004 4600 2
IL
county ST case rank severity R_e cases cases/100k daily cases
McLean IL 15 1 1.6 1735 1000 113
Cook IL 1 2 1.0 126633 2420 700
Lawrence IL 66 3 1.8 131 810 9
McDonough IL 56 4 1.8 200 650 6
Champaign IL 10 5 1.2 2481 1180 66
Lake IL 2 6 1.1 14620 2080 93
Will IL 4 7 1.0 11607 1690 107
DuPage IL 3 8 0.9 14619 1570 105
Madison IL 8 13 1.0 4059 1530 63
McHenry IL 9 17 1.1 3925 1280 36
St. Clair IL 6 18 0.9 5873 2230 63
Winnebago IL 7 22 1.0 4246 1480 26
Kane IL 5 23 0.9 11269 2120 54
IN
county ST case rank severity R_e cases cases/100k daily cases
Delaware IN 17 1 1.7 1158 1000 47
Monroe IN 18 2 1.3 1140 780 35
Marion IN 1 3 1.0 18636 1970 130
Martin IN 79 4 1.5 117 1150 7
St. Joseph IN 4 5 1.0 5255 1950 104
Hamilton IN 6 6 1.1 3949 1250 59
Montgomery IN 44 7 1.5 425 1110 6
Vanderburgh IN 7 8 1.2 2590 1430 34
Lake IN 2 10 1.0 9213 1890 69
Allen IN 5 12 1.0 5029 1360 51
Hendricks IN 8 15 1.1 2357 1460 24
Elkhart IN 3 16 1.0 5702 2800 33
Johnson IN 9 26 1.0 2076 1370 14

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Wayne TN 55 1 3.3 558 3350 89
Shelby TN 1 2 1.1 27612 2950 183
Knox TN 5 3 1.1 6778 1490 94
Trousdale TN 20 4 1.7 1618 16900 4
Fentress TN 76 5 1.4 227 1260 11
Tipton TN 21 6 1.3 1470 2390 20
Robertson TN 14 7 1.2 1888 2720 22
Davidson TN 2 14 0.9 26025 3800 100
Hamilton TN 3 17 0.9 8048 2250 70
Rutherford TN 4 20 0.9 7850 2560 47
Sumner TN 7 22 1.0 4098 2280 30
Wilson TN 8 23 1.0 2832 2130 24
Williamson TN 6 28 0.9 4378 2000 31
Montgomery TN 9 35 0.9 2490 1270 21
KY
county ST case rank severity R_e cases cases/100k daily cases
Todd KY 59 1 2.0 158 1280 20
Rowan KY 57 2 1.8 169 690 12
Fayette KY 2 3 1.1 5677 1780 97
Madison KY 6 4 1.3 1027 1140 36
Jefferson KY 1 5 1.0 12433 1620 165
McCreary KY 79 6 1.5 101 570 6
Warren KY 3 7 1.1 3340 2640 43
Boone KY 5 28 1.0 1297 1000 10
Hardin KY 8 30 1.0 929 860 11
Kenton KY 4 35 0.9 1723 1050 11
Daviess KY 7 39 0.9 990 990 9
Shelby KY 9 58 0.9 904 1930 5

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Gasconade MO 74 1 2.0 98 660 10
Greene MO 5 2 1.3 3370 1170 130
Boone MO 7 3 1.3 2612 1480 99
St. Louis MO 1 4 1.1 19432 1950 213
Madison MO 58 5 1.6 144 1180 13
Nodaway MO 29 6 1.4 420 1860 21
Marion MO 28 7 1.4 477 1660 22
Jackson MO 4 13 1.1 5520 800 66
Jasper MO 8 14 1.2 1628 1370 24
Jefferson MO 6 15 1.1 2811 1260 53
St. Charles MO 3 16 1.0 5773 1480 70
St. Louis city MO 2 27 0.9 6173 1980 34
Clay MO 9 37 1.0 1386 580 16
AR
county ST case rank severity R_e cases cases/100k daily cases
Lincoln AR 11 1 1.7 1604 11710 37
Montgomery AR 67 2 1.8 100 1110 7
Washington AR 2 3 1.4 6890 3010 44
Boone AR 34 4 1.6 357 960 15
Benton AR 3 5 1.3 5361 2070 40
Newton AR 63 6 1.7 120 1530 2
Pulaski AR 1 7 1.0 7104 1810 68
Pope AR 7 9 1.2 1763 2770 26
Craighead AR 6 13 1.1 1870 1770 28
Faulkner AR 9 16 1.1 1701 1390 24
Jefferson AR 5 17 1.0 2122 3010 29
Sebastian AR 4 22 0.9 2890 2270 26
Hot Spring AR 8 41 1.0 1720 5130 5

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1334.9 seconds to compute.
2020-09-01 08:02:04

version history

Today is 2020-09-01.
104 days ago: Multiple states.
96 days ago: \(R_e\) computation.
93 days ago: created color coding for \(R_e\) plots.
88 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
88 days ago: “persistence” time evolution.
81 days ago: “In control” mapping.
81 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
73 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
68 days ago: Added Per Capita US Map.
66 days ago: Deprecated national map.
62 days ago: added state “Hot 10” analysis.
57 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
55 days ago: added per capita disease and mortaility to state-level analysis.
43 days ago: changed to county boundaries on national map for per capita disease.
38 days ago: corrected factor of two error in death trend data.
34 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
29 days ago: added county level “baseline control” and \(R_e\) maps.
25 days ago: fixed normalization error on total disease stats plot.
18 days ago: Corrected some text matching in generating county level plots of \(R_e\).
12 days ago: adapter knot spacing for spline.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.